VKM.test: Chi-square test for the variance of a Normal variable with...

VKM.testR Documentation

Chi-square test for the variance of a Normal variable with known population mean.

Description

Under the assumption that the data come from a Normal distribution, it performs the hypothesis testing and the confidence interval for the variance with known population mean.

Usage

VKM.test(x, sigma = 1, sigmasq = sigma^2, mu,
         alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
         ...)

Arguments

x

a (non-empty) numeric vector of data values.

sigma

a number indicating the true value of the population standard deviation - Null hypothesis.

sigmasq

control argument.

mu

numerical value indicating the population mean assumed to be known (mandatory).

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

conf.level

confidence level of the interval.

...

further arguments to be passed to or from methods.

Value

A list with class "htest" containing the following components:

statistic

the value of the ctest statistic.

parameter

the degrees of freedom for the test statistic.

p.value

the p-value for the test.

conf.int

confidence interval for variance with known population mean associated with the specified alternative hypothesis.

estimate

the estimated variance.

null.value

the specified hypothesized value of the variance.

alternative

a character string describing the alternative hypothesis.

method

a character string indicating what type of statistical test was performed.

data.name

a character string giving the name of the data.

See Also

VUM.test , var.test

Examples

data(cars93) # Dataset provided with the package
# Variance of the maximum price (MaxPrice) assuming that the population mean
# price is known and equal to 22
VKM.test(cars93$MaxPrice, alternative="two.sided", sigma=11, mu=22, conf.level=0.95)

RcmdrPlugin.TeachStat documentation built on Nov. 14, 2023, 5:08 p.m.